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International Journal of Bioresource Science
Year : 2018, Volume : 5, Issue : 1
First page : ( 01) Last page : ( 20)
Print ISSN : 2347-9655. Online ISSN : 2454-9541.
Article DOI : 10.30954/2347–9655.01.2018.1

Advanced Modeling of Soil Biological Properties Using Visible Near Infrared Diffuse Reflectance Spectroscopy

Weindorf David C.1,*, Chakraborty Somsubhra2, Moore-Kucera Jennifer1, Li Bin3, Fultz Lisa4, Acosta-Martinez Veronica5, Li Chenhui1

1Texas Tech University, Lubbock, TX, USA

2Indian Institute of Technology Kharagpur, Kharagpur, India

3Louisiana State University, Baton Rouge, LA, USA

4Louisiana State University, Agricultural Center, Baton Rouge, LA, USA

5Agricultural Research Service, Lubbock, TX, USA

*Corresponding author: david.weindorf@ttu.edu

Online published on 2 March, 2019.

Abstract

Although visible near infrared diffuse reflectance spectroscopy (VisNIR DRS) is an emerging, rapid, non-destructive, and cost effective technology to predict a host of soil biological parameters, the traditional chemometric partial least squares regression (PLS) model often poses challenges during sensor development. In an effort to identify alternatives to the PLS model, three multivariate machine learning algorithms [PLS, penalized spline regression (PSR), and random forest (RF) regression]in conjunction with two spectral preprocessing methods [Savitzky-Golay first derivative and absorbance (ABS)] were compared with respect to 12 soil biological parameters of 123 soil samples. The RF model with ABS spectra successfully predicted all biological parameters with residual prediction deviation (RPD) ranging from 2.60 to 3.60 and outperformed PSR and PLS models. The best PSR model was obtained for total bacteria with an RPD of 2.70 and an r2 of 0.86 and among other variables, only Gram positive bacteria (RPD=2.63, r2=0.85), Gram negative bacteria (RPD=2.58, r2=0.85), and SOM (RPD=2.67, r2=0.86) were satisfactorily predicted, exhibiting r2>0.80 and RPD>2.5. Conversely, all variables except SOM (RPD=2.07) were poorly predicted by PLS models which had an RPD<2. Furthermore, linear discriminant analysis qualitatively clustered soils with different levels of microbial parameters. Summarily, the RF model with ABS spectra showed great promise in characterizing soil microbial communities with potential for such analysis in-situ.

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Keywords

Microbial community, Linear discriminant analysis, Partial least squares, Penalized spline, Random forest, Visible near infrared diffuse reflectance spectroscopy.

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